Abu Zitar, Raed

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Name
Abu Zitar, Raed
Main Affiliation
Email
raed.zitar@sorbonne.ae
ORCID
0000-0003-2693-2132
Scopus Author ID
8858522100
Status
Currently affiliated

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  • Publication
    A Modified Oppositional Chaotic Local Search Strategy Based Aquila Optimizer to Design an Effective Controller for Vehicle Cruise Control System
    (2023)
    Ekinci, Serdar
    ;
    Izci, Davut
    ;
    Abualigah, Laith
    ;
    Abu Zitar, Raed 
    In this work, we propose a real proportional-integral-derivative plus second-order derivative (PIDD2) controller as an efficient controller for vehicle cruise control systems to address the challenging issues related to efficient operation. In this regard, this paper is the first report in the literature demonstrating the implementation of a real PIDD2 controller for controlling the respective system. We construct a novel and efficient metaheuristic algorithm by improving the performance of the Aquila Optimizer via chaotic local search and modified opposition-based learning strategies and use it as an excellently performing tuning mechanism. We also propose a simple yet effective objective function to increase the performance of the proposed algorithm (CmOBL-AO) to adjust the real PIDD2 controller's parameters effectively. We show the CmOBL-AO algorithm to perform better than the differential evolution algorithm, gravitational search algorithm, African vultures optimization, and the Aquila Optimizer using well-known unimodal, multimodal benchmark functions. CEC2019 test suite is also used to perform ablation experiments to reveal the separate contributions of chaotic local search and modified opposition-based learning strategies to the CmOBL-AO algorithm. For the vehicle cruise control system, we confirm the more excellent performance of the proposed method against particle swarm, gray wolf, salp swarm, and original Aquila optimizers using statistical, Wilcoxon signed-rank, time response, robustness, and disturbance rejection analyses. We also use fourteen reported methods in the literature for the vehicle cruise control system to further verify the more promising performance of the CmOBL-AO-based real PIDD2 controller from a wider perspective. The excellent performance of the proposed method is also illustrated through different quality indicators and different operating speeds. Lastly, we also demonstrate the good performing capability of the CmOBL-AO algorithm for real traffic cases. We show the CmOBL-AO-based real PIDD2 controller as the most efficient method to control a vehicle cruise control system.
      26
  • Publication
    A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48
    (2023)
    Abu Zitar, Raed 
    ;
    Al-Manaseer, Hitham
    ;
    Abualigah, Laith
    ;
    Alsoud, Anas Ratib
    ;
    Ezugwu, Absalom E.
    ;
    Jia, Heming
    In this study, the possibility of using and applying the capabilities of artificial intelligence (AI) and machine learning (ML) to increase the effectiveness of Internet of Things (IoT) and big data in developing a system that supports decision makers in the medical fields was studied. This was done by studying the performance of three well-known classification algorithms Random Forest Classifier (RFC), Support Vector Machine (SVM), and Decision Tree-J48 (J48), to predict the probability of heart attack. The performance of the algorithms for accuracy was evaluated using the Healthcare (heart attack possibility) dataset, freely available on kagle. The data was divided into three categories consisting of (303, 909, 1808) instances which were analyzed on the WEKA platform. The results showed that the RFC was the best performer.
      39
  • Publication
    A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education
    (2022)
    Abu Zitar, Raed 
    ;
    AlZu’bi, Shadi
    ;
    Hawashin, Bilal
    ;
    Abu Shanab, Samia
    ;
    Zraiqat, Amjed
    ;
    Mughaid, Ala
    ;
    Almotairi, Khaled H.
    ;
    Abualigah, Laith
    Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest that this technique operates well. Based on emotional analysis, several deep learning techniques are applied to train and test the emotion classification process. Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. The obtained results show that the performance of the proposed method is promising using both techniques, as presented in the Experimental Results Section.
      52  7
  • Publication
    A Novel Methodology for Human Kinematics Motion Detection Based on Smartphones Sensor Data Using Artificial Intelligence
    (2023)
    Raza, Ali
    ;
    Al Nasar, Mohammad Rustom
    ;
    Hanandeh, Essam Said
    ;
    Abu Zitar, Raed 
    ;
    Nasereddin, Ahmad Yacoub
    ;
    Abualigah, Laith
    Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.
      5
  • Publication
    A real-time automatic pothole detection system using convolution neural networks
    (2023)
    Bharat, Ricardo
    ;
    Ikotun, Abiodun M
    ;
    Ezugwu, Absalom E.
    ;
    Abualigah, Laith
    ;
    Shehab, Mohammad
    ;
    Abu Zitar, Raed 
    Detecting a pothole can help prevent damage to your vehicle and potentially prevent an accident. Different techniques, including machine learning, deep learning models, sensor methods, stereo vision, the internet of things (IoT), and black-box cameras, have already been applied to address the problem. However, studies have shown that machine learning and deep learning techniques successfully detect potholes. However, because most of these successful attempts are peculiar to the location of the study, we found no study which has addressed the peculiarity of potholes in South Africa using a tailored-trained deep learning model. In this study, we propose using a convolutional neural network (CNN), a type of deep learning model, to address this growing problem on South African roads. To achieve this, a CNN model was designed from scratch and trained with image samples obtained from the context of the study. The classifier was adapted to distinguish between a binary class which identifies the presence or absence of potholes. Results showed a significant performance enhancement at a classification accuracy of 92.72%. The outcome of this study showed that this machine learning approach holds great potential for addressing the challenge of potholes and road bumps in the region and abroad.
      6
  • Publication
    Adapted arithmetic optimization algorithm for multi-level thresholding image segmentation: a case study of chest x-ray images
    (2023)
    Abu Zitar, Raed 
    ;
    Otair, Mohammad
    ;
    Abualigah, Laith
    ;
    Tawfiq, Saif
    ;
    Alshinwan, Mohammad
    ;
    Ezugwu, Absalom E.
    ;
    Sumari, Putra
    Particularly in recent years, there has been increased interest in determining the ideal thresholding for picture segmentation. The best thresholding values are found using various techniques, including Otsu and Kapur-based techniques. These techniques work well for bi-level thresholding, but when used to find the appropriate thresholds for multi-level thresholding, there will be issues with long calculation times, high computational costs, and the need for accuracy improvements. This work investigates the capability of the Arithmetic Optimization Algorithm to discover the best multilayer thresholding for picture segmentation to circumvent this issue. The leading mathematical arithmetic operators' distributional nature is used by the AOA method. The picture histogram was used to construct the candidate solutions in the modified algorithms, which were then updated according to the algorithm's features. The solutions are evaluated using Otsu's fitness function throughout the optimization process. The picture histogram is used to display the algorithm's potential solutions. The proposed approach is tested on five frequent photos from the Berkeley University database. The fitness function, root-mean-squared error, peak signal-to-noise ratio, and other widely used assessment metrics were utilized to assess the performance of the suggested segmentation approach. Many benchmark pictures were employed to verify the suggested technique's effectiveness and evaluate it against other well-known optimization methods described in the literature.
      15
  • Publication
    An intelligent cybersecurity system for detecting fake news in social media websites
    (2022)
    Zitar, Raed 
    ;
    Mughaid, Ala
    ;
    Al-Zu'bi, Shadi
    ;
    Arjan, A
    ;
    Al-Amrat, Rula
    ;
    Alajmi, Rathaa
    ;
    Abualigah, Laith
    ;
    Maalej, Ahmed
    People worldwide suffer from fake news in many life aspects, healthcare, transportation, education, economics, and many others. Therefore, many researchers have considered seeking techniques for automatically detecting fake news in the last decade. The most popular news agencies use e-publishing on their websites; even websites can publish any news they want. However, thus before quotation any news from a website, there should be a close look at news resource ranking by using a trusted websites classifier, such as the website world rank, which reflects the repute of these websites. This paper uses the world rank of news websites as the main factor of news accuracy by using two widespread and trusted websites ranking. Moreover, a secondary factor is proposed to compute the news accuracy similarity by comparing the current news with fakes news and getting the possible news accuracy. Experiments results are conducted on several benchmark datasets. The results showed that the proposed method got promising results compared to other comparative methods in defining the news accuracy.
    Scopus© Citations 3  42  60
  • Publication
    Application of Red Deer Algorithm in Optimizing Complex functions
    (2021)
    Zitar, Raed 
    ;
    Abualigah, Laith
    The Red Deer algorithm (RDA), a recently developed population-based meta-heuristic algorithm, is examined in this paper with the optimization task of complex functions. The RD algorithm blends evolutionary algorithms' survival of the fittest concept with heuristic search techniques' productivity and richness. It is critical to assess this algorithm's performance in comparison with other well-known heuristic methods. The findings are presented along with additional recommendations for increasing RDA performance based on the analysis. The readers of this paper will gain a grasp of the RD algorithm and its optimization ability to determine whether this algorithm is appropriate for their particular business, research, or industrial needs.
    Scopus© Citations 2  19
  • Publication
    Arabic Text Classification Using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect
    (2023)
    Abu Zitar, Raed 
    ;
    Habeeb, Abdallah
    ;
    Otair, Mohammed A
    ;
    Abualigah, Laith
    ;
    Alsoud, Anas Ratib
    ;
    Elminaam, Diaa Salama Abd
    ;
    Ezugwu, Absalom E
    ;
    Jia, Heming
    Arab customers give their comments and opinions daily, and it increases dramatically through online reviews of products or services from companies, in both Arabic, and its dialects. This text describes the user’s condition or needs for satisfaction or dissatisfaction, and this evaluation is either negative or positive polarity. Based on the need to work on Arabic text sentiment analysis problem, the case of the Jordanian dialect. The main purpose of this paper is to classify text into two classes: negative or positive which may help the business to maintain a report about service or product. The first phase has tools used in natural language processing; the stemming, stop word removal, and tokenization to filtering the text. The second phase, modified the Artificial Bee Colony (ABC) Algorithm, with Upper Confidence Bound (UCB) Algorithm, to promote the exploitation ability for the minimum dimension, to get the minimum number of the optimal feature, then using forward feature selection strategy by four classifiers of machine learning algorithms: (K-Nearest Neighbors (KNN), Support vector machines (SVM), Naïve-Bayes (NB), and Polynomial Neural Networks (PNN). This proposed model has been applied to the Jordanian dialect database, which contains comments from Jordanian telecom company’s customers. Based on the results of sentiment analysis few suggestions can be provided to the products or services to discontinue or drop, or upgrades it. Moreover, the proposed model is applied to the database of the Algerian dialect, which contains long Arabic texts, in order to see the efficiency of the proposed model for short and long texts. Four performance evaluation criteria were used: precision, recall, f1-score, and accuracy. For a future step, in order to build on or use for the classification of Arabic dialects, the experimental results show that the proposed model gives height accuracy up to 99% by applying to the Jordanian dialect, and a 82% by applying to the Algerian dialect.
      19
  • Publication
    Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
    (2022)
    Zitar, Raed 
    ;
    Abualigah, Laith
    ;
    Abd Elaziz, Mohamed
    ;
    Yousri, Dalia
    ;
    Al-qaness, Mohammed A. A.
    ;
    Ewees, Ahmed A.
    This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization algorithm, called arithmetic optimization algorithm (AOA), was proposed to address complex optimization tasks. Math operations inspire the AOA, and it showed significant performance in dealing with different optimization problems. However, the traditional AOA faces some limitations in its search process. Thus, we develop a new variant of the AOA, namely, Augmented AOA (AAOA), integrated with the opposition-based learning (OLB) and Lévy flight (LF) distribution. The main idea of applying OLB and LF is to improve the traditional AOA exploration and exploitation trends in order to find the best clusters. To evaluate the AAOA, we implemented extensive experiments using twenty-three well-known benchmark functions and eight data clustering datasets. We also evaluated the proposed AAOA with extensive comparisons to different optimization algorithms. The outcomes verified the superiority of the AAOA over the traditional AOA and several MH optimization algorithms. Overall, the applications of the LF and OLB have a significant impact on the performance of the conventional AOA.
      29  1